Literature DB >> 32172439

Multicenter external validation of the Zurich Pituitary Score.

Victor E Staartjes1, Carlo Serra2, Matteo Zoli3, Diego Mazzatenta3, Fabio Pozzi4, Davide Locatelli4, Elena D'Avella5, Domenico Solari5, Luigi Maria Cavallo5, Luca Regli2.   

Abstract

PURPOSE: Recently, the Zurich Pituitary Score (ZPS) has been proposed as a new quantitative preoperative classification scheme for predicting gross total resection (GTR), extent of resection (EOR), and residual tumor volume (RV) in endoscopic pituitary surgery. We evaluated the external validity of the ZPS.
METHODS: In three reference centers for pituitary surgery, the ZPS was applied and correlated to GTR, EOR, and RV. Furthermore, its inter-rater agreement was assessed.
RESULTS: A total of 485 patients (53% male; age, 53.8 ± 15.7) were included. ZPS grades I, II, III, and IV were observed in 110 (23%), 270 (56%), 64 (13%), and 41 (8%) patients, respectively. GTR was achieved in 358 (74%) cases, with mean EOR of 87.6% ± 20.3% and RV of 1.42 ± 2.80 cm3. With increasing ZPS grade, strongly significant decreasing trends for GTR (I, 92%; II, 77%; III, 67%; IV, 15%; p < 0.001) and EOR (I, 93.8%; II, 89.9%; III, 88.1%; IV, 75.4%; p < 0.001) were found. Similarly, RV increased steadily ([cm3] I, 0.16; II, 0.61; III, 2.01; IV, 3.84; p < 0.001). We observed intraclass correlation coefficients of 0.837 (95% CI, 0.804-0.865) for intercarotid distance and 0.964 (95% CI, 0.956-0.970) for adenoma diameter, and Cohen's kappa of 0.972 (95% CI, 0.952-0.992) for the ZPS grades.
CONCLUSIONS: Application of the ZPS in three external cohorts was successful. The ZPS generalized well in terms of GTR, EOR, and RV; demonstrated excellent inter-rater agreement; and can safely and effectively be applied as a quantitative classification of adenomas with relevance to surgical outcome.

Entities:  

Keywords:  Extent of resection; External validation; Gross total resection; Outcome prediction; Pituitary adenoma; Zurich Pituitary Score

Mesh:

Year:  2020        PMID: 32172439     DOI: 10.1007/s00701-020-04286-w

Source DB:  PubMed          Journal:  Acta Neurochir (Wien)        ISSN: 0001-6268            Impact factor:   2.216


  4 in total

Review 1.  Machine Learning in Pituitary Surgery.

Authors:  Vittorio Stumpo; Victor E Staartjes; Luca Regli; Carlo Serra
Journal:  Acta Neurochir Suppl       Date:  2022

Review 2.  Impact of intraoperative magnetic resonance imaging on gross total resection, extent of resection, and residual tumor volume in pituitary surgery: systematic review and meta-analysis.

Authors:  Victor E Staartjes; Alex Togni-Pogliorini; Vittorio Stumpo; Carlo Serra; Luca Regli
Journal:  Pituitary       Date:  2021-05-04       Impact factor: 4.107

3.  Radiological Knosp, Revised-Knosp, and Hardy-Wilson Classifications for the Prediction of Surgical Outcomes in the Endoscopic Endonasal Surgery of Pituitary Adenomas: Study of 228 Cases.

Authors:  Marta Araujo-Castro; Alberto Acitores Cancela; Carlos Vior; Eider Pascual-Corrales; Víctor Rodríguez Berrocal
Journal:  Front Oncol       Date:  2022-01-20       Impact factor: 6.244

4.  Machine learning-based clinical outcome prediction in surgery for acromegaly.

Authors:  Olivier Zanier; Matteo Zoli; Victor E Staartjes; Federica Guaraldi; Sofia Asioli; Arianna Rustici; Valentino Marino Picciola; Ernesto Pasquini; Marco Faustini-Fustini; Zoran Erlic; Luca Regli; Diego Mazzatenta; Carlo Serra
Journal:  Endocrine       Date:  2021-10-12       Impact factor: 3.633

  4 in total

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